Publications
The sustainability of resource use and the management of public finances are both long-run issues that are linked to each other through savings decisions. To study them conjointly, this paper introduces a public debt stabilization constraint in an overlapping generation model in which nonrenewable resources constitute a necessary input in the production function and belong to agents. It shows that stabilization of public debt at a high level (as share of capital) may prevent the existence of a sustainable development path, that is, a path on which per capita consumption is not decreasing. Public debt thus appears as a threat to sustainable development. It also shows that higher public debt-to-capital ratios (and public expenditures-to-capital ones) are associated with lower growth. Two transmission channels are identified. As usual, public debt crowds out capital accumulation. In addition, public debt tends to increase resource use which reduces the rate of growth. We also provide a numerical analysis of the dynamics that shows that the economy is characterized by saddle path stability. Finally, we show that the public debt-to-capital ratio may be calibrated to implement the social planner optimal allocation according to which the growth rate is increasing in the degree of patience.
Sans résumé.
Several papers explain why asset bubbles are observed when growth is large. These papers differ in the role of the bubble, used to provide liquidities or as collateral in a borrowing constraint. We compare the liquidity and collateral roles of bubbles in an overlapping generations model. When the bubble is deterministic, the equilibrium is identical under these two roles, implying that the same mechanism explains the crowding-in effect of the bubble on growth. With stochastic bubbles, growth is larger when bubbles play the liquidity role, because the burst of a bubble used for liquidity is less damaging to capital investors.
Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.
In France, the national health insurance (NHI) covers the majority of health expenses, the rest being more or less covered by the complementary health insurance (CHI). But do insured persons really know their CHI contract? This empirical work explores administrative data matched with survey data providing information on usually unobserved characteristics (health status or risk aversion). First, we identify the factors that influence policyholders’ knowledge of their contract, then we analyze the gap between reimbursements and contributions using quantile regressions. Paradoxically, this gap does not seem to be widened by the consumption of so-called “comfort” care but rather by hospital care. Moreover, a better knowledge of one’s contract seems to be beneficial.
Mexican cities along the US-Mexico border, especially Cd. Juarez, became notorious due to high femicide rates supposedly associated with maquiladora industries and the NAFTA. Nonetheless, statistical evaluation of data from 1990 to 2012 shows that their rates are consistent with other Mexican cities’ rates and tend to fall with increased employment opportunities in maquiladoras. Femicide rates in Cd. Juarez are in most years like rates in Cd. Chihuahua and Ensenada and, as a share of overall homicide rates, are lower than in most cities evaluated. These results challenge conventional wisdom and most of the literature on the subject.
In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model. Some researchers have tried to adjust other types of models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vector machines, neural networks and also direct multi-output approaches. We survey the subject and illustrate the use of ten such approaches using simulations and a real use case. In our simulations, the underlying model is linear in the explanatory factors for one response and nonlinear for the others. We focus on the advantages and disadvantages of the different approaches and show how their hyperparameters may be tuned. Our simulations show that even when the underlying relation between the response and the explanatory variables is linear, the RSM approach is outperformed by the direct neural network multivariate model, for any sample size (<50) and much more for very small samples (15 or 20). When the underlying relation is nonlinear, the RSM approach is outperformed by most of the machine learning approaches for small samples (n ≤ 30).
The popular view is that governments should crack down on tax avoidance by multinational corporations, but in practice, lax anti-profit-shifting policies are common. Here, we analyze how controlling profit shifting influences fiscal competition. Equilibrium tax rates are determined by the elasticities of two components: retained profit and capital mobility. Anti-profit-shifting policies decrease the elasticity of the first, but increase the elasticity of the second. The impact of these policies on equilibrium tax rates is then ambiguous. We show that there are cases in which laxer policies increase equilibrium tax rates and countries’ well-being by favoring investments. We use estimates of different elasticities to show that our model can support lax enforcement.
The paper examines the question of non-anonymous Growth Incidence Curves (na-GIC) from a Bayesian inferential point of view. Building on the notion of conditional quantiles of Barnett (1976. “The Ordering of Multivariate Data.” Journal of the Royal Statistical Society: Series A 139: 318–55), we show that removing the anonymity axiom leads to a complex and shaky curve that has to be smoothed, using a non-parametric approach. We opted for a Bayesian approach using Bernstein polynomials which provides confidence intervals, tests and a simple way to compare two na-GICs. The methodology is applied to examine wage dynamics in a US university with a particular attention devoted to unbundling and anti-discrimination policies. Our findings are the detection of wage scale compression for higher quantiles for all academics and an apparent pro-female wage increase compared to males. But this pro-female policy works only for academics and not for the para-academics categories created by the unbundling policy.
This paper highlights how technology can contribute to reaching the 2015 Paris Agreement goals of net zero carbon dioxide (CO2) emissions and global warming below 2°C in 2100. It uses the Advanced Climate Change Long-term model (ACCL), particularly adapted to quantify the consequences of energy price and technology shocks on CO2 emissions, temperature, climate damage and Gross Domestic Product (GDP). The simulations show that without climate policies the warming may be +5°C in 2100, with considerable climate damage. An acceleration in ‘usual’ technical progress not targeted at reducing CO2- even worsens global warming and climate damage. According to our estimates, the world does not achieve climate goals in 2100 without ‘green’ technologies. Intervening only via energy prices, e.g. a carbon tax, requires challenging hypotheses of international coordination and price increase for polluting energies. We assess a multi-lever climate strategy combining energy efficiency gains, carbon sequestration, and a decrease of 3% per year in the relative price of ‘clean’ electricity with a 1 to 1.5% annual rise in the relative price of polluting energy sources. None of these components alone is sufficient to reach climate objectives. Our last and most important finding is that our composite scenario achieves the climate goals.
